EFRI BRAID: Brain-inspired Algorithms for Autonomous Robots (BAAR)

自主机器人，如自动驾驶汽车（sdv）和家庭协作机器人（Cobots），具有巨大的潜力，可以造福社会，满足几个重要的国家需求。尽管人工智能（AI）已经取得了实质性进展，但在执行驾驶和烹饪等常规感觉运动任务时，自主机器人目前的数据/计算效率和适应性与人类相比相形见绌。使这种自主机器人能够像人类一样不断地从经验中学习，并不断提高它们在现实世界中的效率和弹性，这是它们广泛部署的必要条件。本项目旨在利用神经生物学学习和大脑智能的原理和见解，为机器人自主开发新的计算算法。其结果可能会对自主机器人产生多方面的变革性影响，例如sdv、协作机器人以及制造业和医疗保健应用中的其他智能机器人系统，这些机器人面临着计算/数据效率低下和适应性不灵活的挑战。该项目旨在通过将大脑启发的智能融入自主机器人系统的感知、规划和持续学习的基本和核心能力，为自主机器人系统提供范式转变。该项目采用融合工程科学的方法，旨在为自主机器人创建一个基于大脑启发的感知、学习和规划算法的基础和创新框架。该框架将通过理论和实证相结合的研究，应用于sdv和Cobots这两个具有代表性和互补性的工程系统。整合大脑启发的创新，这项工作将适应和设计通用的大脑启发方法和算法，用于sdv和协作机器人，以实验验证数据和能源效率、适应性和弹性的有效性。预计这些发现不仅将为sdv和协作机器人在现实世界中的部署提供重大飞跃，而且还将通过提高数据/计算效率、适应弹性和智能可解释性，对其他智能机器人系统（如制造业和医疗保健领域的智能机器人系统）产生变革性影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Autonomous robots, such as self-driving vehicles (SDVs) and household collaborative robots (Cobots), possess great potential to benefit society and meet several important national needs. Although artificial intelligence (AI) has made substantial progress, the current data/computational efficiency and adaptability of autonomous robots pale in comparison to humans in performing routine sensorimotor tasks such as driving and cooking. Enabling such autonomous robots to continually learn from experience and persistently improve their efficiency and resilience in the real world as humans do is imperative for their widespread deployments. This project aims to develop novel computational algorithms for robot autonomy with principles and insights of neurobiological learning and brain intelligence. The outcomes could make a multifaceted and transformative impact on autonomous robots such as SDVs, Cobots, and other intelligent robotic systems in manufacturing and healthcare applications that face the same challenges of computational/data inefficiency and adaptation inflexibility.The project seeks to provide a paradigm shift in autonomous robotic systems by incorporating brain-inspired intelligence throughout their fundamental and core capabilities of perception, planning, and continual learning. Using convergent engineering-science approaches, the project aims to create a fundamental and innovative framework of brain-inspired perception, learning, and planning algorithms for autonomous robots. The framework will be applied to SDVs and Cobots as two representative and complementary engineering systems through combined theoretical and empirical studies. Integrating brain-inspired innovations, the work will adapt and engineer the general brain-inspired methods and algorithms to SDVs and Cobots for experimental validation of the effectiveness in data- and energy-efficiency, adaptability, and resiliency. It is expected that the findings will not only provide a significant leap to SDVs and Cobots toward their real-world deployments, but also have a transformative impact on other intelligent robotic systems such as those in manufacturing and healthcare domains by improving their data/computation efficiency, adaptation resiliency, and intelligence interpretability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.